A Anne Rozinat

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Decision mining in prom

Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.

Conformance testing: measuring the fit and appropriateness of event logs and process models

Most information systems log events (e.g., transaction logs, audit trails) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: “Does the event log conform to the process model and vice versa?”. This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework.

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a colored Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models.

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflows design, but also use logged data describing the systems observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.

An outlook on semantic business process mining and monitoring

Semantic Business Process Management (SBPM) has been proposed as an extension of BPM with Semantic Web and Semantic Web Services (SWS) technologies in order to increase and enhance the level of automation that can be achieved within the BPM life-cycle. In a nutshell, SBPM is based on the extensive and exhaustive conceptualization of the BPM domain so as to support reasoning during business processes modelling, composition, execution, and analysis, leading to important enhancements throughout the life-cycle of business processes. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. This analysis provides feedback about how these processes are actually being executed (like common control-flow paths, performance measures, detection of bottlenecks, alert to approaching deadlines, auditing, etc). The use of semantic information can lead to dramatic enhancements in the state-of-the-art in analysis techniques. In this paper we present an outlook on the opportunities and challenges on semantic business process mining and monitoring, thus paving the way for the implementation of the next generation of BPM analysis tools.

Process Mining Applied to the Test Process of Wafer Scanners in ASML

Process mining techniques attempt to extract nontrivial and useful information from event logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities. Several successful case studies have been reported in literature, all demonstrating the applicability of process mining. However, these case studies refer to rather structured administrative processes. In this paper, we investigate the applicability of process mining to less structured processes. We report on a case study where the process mining (ProM) framework has been applied to the test processes of ASML (the leading manufacturer of wafer scanners in the world).This case study provides many interesting insights. On the one hand, process mining is also applicable to the less structured processes of ASML. On the other hand, the case study also shows the need for alternative mining approaches.

The need for a process mining evaluation framework in research and practice

Although there has been much progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results.

Activity Mining by Global Trace Segmentation

Process Mining is a technology for extracting non-trivial and useful information from execution logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities . Unfortunately, the quality of a discovered process model strongly depends on the quality and suitability of the input data. For example, the logs of many real-life systems do not refer to the activities an analyst would have in mind, but are on a much more detailed level of abstraction. Trace segmentation attempts to group low-level events into clusters, which represent the execution of a higher-level activity in the (available or imagined) process meta-model. As a result, the simplified log can be used to discover better process models. This paper presents a new activity mining approach based on global trace segmentation. We also present an implementation of the approach, and we validate it using a real-life event log from ASML’s test process.

Discovering colored Petri nets from event logs

Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns.